When transmitting high speed serial data over non-ideal communication channels, many environments experience signal attenuation and reflections caused by impedance mismatches. It's common practice to use a combination of equalization schemes such as linear equalizers and Decision Feedback Equalizers (DFE) to compensate for these effects. In many environments, the non-ideal nature of these channels can be measured or modeled to aid in determining which equalization settings will provide the best compensation. In many other environments, however, the non-ideal nature of these channels can not be measured or modeled. In these environments, the equalization settings cannot be precalculated to their optimal values. These environments require dynamic programming of the equalization settings based on real time analysis of either the environment, or of the recoverability of the traffic.
Others have attempted to solve this problem in a number of different ways. First, R. W. Lucky of Bell Labs introduced a process in 1965 for being able to equalize a signal without any prior knowledge of the transmitted signal. The process is described in a textbook ADAPTIVE SIGNAL PROCESSING by Widrow and Stearns, published in 1985 by Prentice Hall (pages 247-249). The process is known as decision-directed learning. This process, however, uses quantizers to adjust necessary equalization procedures. U.S. Pat. No. 5,539,774 describes a method for using a least mean squares process for determining an error rate, and then adjusting the equalizer parameters.
Neither of these methods, however, solves the problem in the manner contemplated in accordance with the present invention.